Optimized deep network based spoof detection in automatic speaker verification system

Speaker-verification-system (SVS) is automated nowadays to improve the authenticity score of digital applications. However, spoofs in the audio signal have reduced the integrity score of the audio signal, which has tended to cause less authentication exactness score. Considering this, spoof recognition objectives emerged in this field to find the different types of spoofs with high exactness scores. Attracting the widest spoof forecasting score is impossible due to harmful and different spoof features. So, the present study built a novel Dove-based Recurrent Spoof Recognition System (DbRSRS) to identify the spoofing behaviour and its types from the trained audio data. The noise features were filtered in the primary stage to mitigate the complexity of spoof recognition. Moreover, the noise features filtered data is taken to the classification phase for feature selection and spoof recognition. Here, the spoof types were classified based on the different class features. Once the Spoof is identified, it is specified under different spoof classes. Here, the optimal dove features are utilized to tune the DbRSRS classification parameters. This process helped to earn the finest spoof recognition score than the recently published associated model. Henceforth, the recorded highest spoof forecasting accuracy was 99.2%, and the reported less error value was 0.05%. Thus, attaining the highest spoof prediction exactness score with less error value might improve the SVS performance.